{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe thead th {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SP500</th>\n",
       "      <th>USDRMB</th>\n",
       "      <th>SHINDEX</th>\n",
       "      <th>SZINDEX</th>\n",
       "      <th>USDINDEX</th>\n",
       "      <th>EURUSD</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-01-03 00:00:00</th>\n",
       "      <td>1455.22</td>\n",
       "      <td>8.2795</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>114.4743</td>\n",
       "      <td>1.0273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-04 00:00:00</th>\n",
       "      <td>1399.42</td>\n",
       "      <td>8.2798</td>\n",
       "      <td>1406.371</td>\n",
       "      <td>3497.060</td>\n",
       "      <td>114.3583</td>\n",
       "      <td>1.0295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-05 00:00:00</th>\n",
       "      <td>1402.11</td>\n",
       "      <td>8.2799</td>\n",
       "      <td>1409.682</td>\n",
       "      <td>3486.285</td>\n",
       "      <td>114.6221</td>\n",
       "      <td>1.0316</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       SP500  USDRMB   SHINDEX   SZINDEX  USDINDEX  EURUSD\n",
       "Date                                                                      \n",
       "2000-01-03 00:00:00  1455.22  8.2795       NaN       NaN  114.4743  1.0273\n",
       "2000-01-04 00:00:00  1399.42  8.2798  1406.371  3497.060  114.3583  1.0295\n",
       "2000-01-05 00:00:00  1402.11  8.2799  1409.682  3486.285  114.6221  1.0316"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "df = pd.read_excel(\"yyf_prices.xls\",parse_dates=[0])\n",
    "#df = pd.read_csv(\"yyf_prices.csv\",parse_dates=[0])\n",
    "df.index=df.pop('Date')\n",
    "df.head(3)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SP500</th>\n",
       "      <th>USDRMB</th>\n",
       "      <th>SHINDEX</th>\n",
       "      <th>SZINDEX</th>\n",
       "      <th>USDINDEX</th>\n",
       "      <th>EURUSD</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-08-23 00:00:00</th>\n",
       "      <td>2444.04</td>\n",
       "      <td>6.6633</td>\n",
       "      <td>3287.7049</td>\n",
       "      <td>10619.3368</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.1807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-24 00:00:00</th>\n",
       "      <td>2438.97</td>\n",
       "      <td>6.6525</td>\n",
       "      <td>3271.5117</td>\n",
       "      <td>10552.9621</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.1799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-25 00:00:00</th>\n",
       "      <td>2443.05</td>\n",
       "      <td>6.6579</td>\n",
       "      <td>3331.5221</td>\n",
       "      <td>10659.0085</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.1923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaN</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>数据来源:Wind资讯</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       SP500  USDRMB    SHINDEX     SZINDEX  USDINDEX  EURUSD\n",
       "Date                                                                         \n",
       "2017-08-23 00:00:00  2444.04  6.6633  3287.7049  10619.3368       NaN  1.1807\n",
       "2017-08-24 00:00:00  2438.97  6.6525  3271.5117  10552.9621       NaN  1.1799\n",
       "2017-08-25 00:00:00  2443.05  6.6579  3331.5221  10659.0085       NaN  1.1923\n",
       "NaN                      NaN     NaN        NaN         NaN       NaN     NaN\n",
       "数据来源:Wind资讯              NaN     NaN        NaN         NaN       NaN     NaN"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 4614 entries, 2000-01-03 00:00:00 to 数据来源:Wind资讯\n",
      "Data columns (total 6 columns):\n",
      "SP500       4441 non-null float64\n",
      "USDRMB      4305 non-null float64\n",
      "SHINDEX     4274 non-null float64\n",
      "SZINDEX     4274 non-null float64\n",
      "USDINDEX    4445 non-null float64\n",
      "EURUSD      4592 non-null float64\n",
      "dtypes: float64(6)\n",
      "memory usage: 252.3+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
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       "    .dataframe thead th {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SP500</th>\n",
       "      <th>USDRMB</th>\n",
       "      <th>SHINDEX</th>\n",
       "      <th>SZINDEX</th>\n",
       "      <th>USDINDEX</th>\n",
       "      <th>EURUSD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4441.000000</td>\n",
       "      <td>4305.000000</td>\n",
       "      <td>4274.000000</td>\n",
       "      <td>4274.000000</td>\n",
       "      <td>4445.000000</td>\n",
       "      <td>4592.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1413.358131</td>\n",
       "      <td>7.261654</td>\n",
       "      <td>2390.367939</td>\n",
       "      <td>7936.923814</td>\n",
       "      <td>110.963164</td>\n",
       "      <td>1.216617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>402.768955</td>\n",
       "      <td>0.851344</td>\n",
       "      <td>915.039787</td>\n",
       "      <td>3992.749709</td>\n",
       "      <td>10.060243</td>\n",
       "      <td>0.175403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>676.530000</td>\n",
       "      <td>6.093000</td>\n",
       "      <td>1011.499000</td>\n",
       "      <td>2622.026000</td>\n",
       "      <td>93.757100</td>\n",
       "      <td>0.826800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1131.420000</td>\n",
       "      <td>6.461600</td>\n",
       "      <td>1667.578750</td>\n",
       "      <td>3820.805250</td>\n",
       "      <td>101.847500</td>\n",
       "      <td>1.099875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1309.720000</td>\n",
       "      <td>6.879800</td>\n",
       "      <td>2199.461000</td>\n",
       "      <td>8235.478000</td>\n",
       "      <td>110.025600</td>\n",
       "      <td>1.254100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1562.170000</td>\n",
       "      <td>8.276600</td>\n",
       "      <td>2976.678750</td>\n",
       "      <td>10701.904750</td>\n",
       "      <td>119.976400</td>\n",
       "      <td>1.343250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2480.910000</td>\n",
       "      <td>8.279900</td>\n",
       "      <td>6092.057000</td>\n",
       "      <td>19531.155000</td>\n",
       "      <td>130.242000</td>\n",
       "      <td>1.599800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             SP500       USDRMB      SHINDEX       SZINDEX     USDINDEX  \\\n",
       "count  4441.000000  4305.000000  4274.000000   4274.000000  4445.000000   \n",
       "mean   1413.358131     7.261654  2390.367939   7936.923814   110.963164   \n",
       "std     402.768955     0.851344   915.039787   3992.749709    10.060243   \n",
       "min     676.530000     6.093000  1011.499000   2622.026000    93.757100   \n",
       "25%    1131.420000     6.461600  1667.578750   3820.805250   101.847500   \n",
       "50%    1309.720000     6.879800  2199.461000   8235.478000   110.025600   \n",
       "75%    1562.170000     8.276600  2976.678750  10701.904750   119.976400   \n",
       "max    2480.910000     8.279900  6092.057000  19531.155000   130.242000   \n",
       "\n",
       "            EURUSD  \n",
       "count  4592.000000  \n",
       "mean      1.216617  \n",
       "std       0.175403  \n",
       "min       0.826800  \n",
       "25%       1.099875  \n",
       "50%       1.254100  \n",
       "75%       1.343250  \n",
       "max       1.599800  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SP500</th>\n",
       "      <th>USDRMB</th>\n",
       "      <th>SHINDEX</th>\n",
       "      <th>SZINDEX</th>\n",
       "      <th>USDINDEX</th>\n",
       "      <th>EURUSD</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-01-03 00:00:00</th>\n",
       "      <td>1455.22</td>\n",
       "      <td>8.2795</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>114.4743</td>\n",
       "      <td>1.0273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-04 00:00:00</th>\n",
       "      <td>1399.42</td>\n",
       "      <td>8.2798</td>\n",
       "      <td>1406.371</td>\n",
       "      <td>3497.060</td>\n",
       "      <td>114.3583</td>\n",
       "      <td>1.0295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-05 00:00:00</th>\n",
       "      <td>1402.11</td>\n",
       "      <td>8.2799</td>\n",
       "      <td>1409.682</td>\n",
       "      <td>3486.285</td>\n",
       "      <td>114.6221</td>\n",
       "      <td>1.0316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-06 00:00:00</th>\n",
       "      <td>1403.45</td>\n",
       "      <td>8.2798</td>\n",
       "      <td>1463.942</td>\n",
       "      <td>3655.199</td>\n",
       "      <td>115.1688</td>\n",
       "      <td>1.0295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-07 00:00:00</th>\n",
       "      <td>1441.47</td>\n",
       "      <td>8.2797</td>\n",
       "      <td>1516.604</td>\n",
       "      <td>3828.040</td>\n",
       "      <td>114.9098</td>\n",
       "      <td>1.0280</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       SP500  USDRMB   SHINDEX   SZINDEX  USDINDEX  EURUSD\n",
       "Date                                                                      \n",
       "2000-01-03 00:00:00  1455.22  8.2795       NaN       NaN  114.4743  1.0273\n",
       "2000-01-04 00:00:00  1399.42  8.2798  1406.371  3497.060  114.3583  1.0295\n",
       "2000-01-05 00:00:00  1402.11  8.2799  1409.682  3486.285  114.6221  1.0316\n",
       "2000-01-06 00:00:00  1403.45  8.2798  1463.942  3655.199  115.1688  1.0295\n",
       "2000-01-07 00:00:00  1441.47  8.2797  1516.604  3828.040  114.9098  1.0280"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SP500       4441\n",
       "USDRMB      4305\n",
       "SHINDEX     4274\n",
       "SZINDEX     4274\n",
       "USDINDEX    4445\n",
       "EURUSD      4592\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SP500      -0.185599\n",
       "USDRMB     -1.690454\n",
       "SHINDEX     1.389385\n",
       "SZINDEX    -0.721929\n",
       "USDINDEX   -1.272336\n",
       "EURUSD     -0.588045\n",
       "dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.kurt()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x21c176636a0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ELz0VviClxJqXh9mYzby2Pyc1QMMXo/UAIdwV+rS1P/bYY2zcuFEvNDZy5EiP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\nRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x21c1780e470>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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GEh0dzd13381ff1nq03ft2pXmzZubrzF48GAAnnzySV5//XWr8Y0dO9bXP4YSzSt/vgLA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\n5Q4VzOSbJztMoVMlpAp7RuwxK6vlg5YTERpBaGC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bC3jh+9XMCKFELM47pgVPnNWNcm8l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\nMRgMBkONYZSMwWAwGGqM/wc2ljRZYn3epgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.SP500.plot(legend='SP500')\n",
    "df.SHINDEX.plot(legend='SHINDEX')\n",
    "df.SZINDEX.plot(legend='SZINDEX')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SP500</th>\n",
       "      <th>USDRMB</th>\n",
       "      <th>SHINDEX</th>\n",
       "      <th>SZINDEX</th>\n",
       "      <th>USDINDEX</th>\n",
       "      <th>EURUSD</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-01-04 00:00:00</th>\n",
       "      <td>1399.42</td>\n",
       "      <td>8.2798</td>\n",
       "      <td>1406.371</td>\n",
       "      <td>3497.060</td>\n",
       "      <td>114.3583</td>\n",
       "      <td>1.0295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-05 00:00:00</th>\n",
       "      <td>1402.11</td>\n",
       "      <td>8.2799</td>\n",
       "      <td>1409.682</td>\n",
       "      <td>3486.285</td>\n",
       "      <td>114.6221</td>\n",
       "      <td>1.0316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-06 00:00:00</th>\n",
       "      <td>1403.45</td>\n",
       "      <td>8.2798</td>\n",
       "      <td>1463.942</td>\n",
       "      <td>3655.199</td>\n",
       "      <td>115.1688</td>\n",
       "      <td>1.0295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-07 00:00:00</th>\n",
       "      <td>1441.47</td>\n",
       "      <td>8.2797</td>\n",
       "      <td>1516.604</td>\n",
       "      <td>3828.040</td>\n",
       "      <td>114.9098</td>\n",
       "      <td>1.0280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-10 00:00:00</th>\n",
       "      <td>1457.60</td>\n",
       "      <td>8.2794</td>\n",
       "      <td>1545.112</td>\n",
       "      <td>3921.484</td>\n",
       "      <td>114.9799</td>\n",
       "      <td>1.0253</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       SP500  USDRMB   SHINDEX   SZINDEX  USDINDEX  EURUSD\n",
       "Date                                                                      \n",
       "2000-01-04 00:00:00  1399.42  8.2798  1406.371  3497.060  114.3583  1.0295\n",
       "2000-01-05 00:00:00  1402.11  8.2799  1409.682  3486.285  114.6221  1.0316\n",
       "2000-01-06 00:00:00  1403.45  8.2798  1463.942  3655.199  115.1688  1.0295\n",
       "2000-01-07 00:00:00  1441.47  8.2797  1516.604  3828.040  114.9098  1.0280\n",
       "2000-01-10 00:00:00  1457.60  8.2794  1545.112  3921.484  114.9799  1.0253"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df=df.dropna()\n",
    "new_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SP500       1441.4700\n",
       "USDRMB         8.2797\n",
       "SHINDEX     1516.6040\n",
       "SZINDEX     3828.0400\n",
       "USDINDEX     114.9098\n",
       "EURUSD         1.0280\n",
       "Name: 2000-01-07 00:00:00, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.iloc[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2000-01-04 00:00:00    1.0295\n",
       "2000-01-05 00:00:00    1.0316\n",
       "2000-01-06 00:00:00    1.0295\n",
       "2000-01-07 00:00:00    1.0280\n",
       "2000-01-10 00:00:00    1.0253\n",
       "Name: EURUSD, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.iloc[:,5].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 1000 entries, 2000-01-06 00:00:00 to 2004-05-25 00:00:00\n",
      "Data columns (total 1 columns):\n",
      "tmp_df    1000 non-null float64\n",
      "dtypes: float64(1)\n",
      "memory usage: 15.6+ KB\n"
     ]
    }
   ],
   "source": [
    "tmp_data=np.random.randn(1000,1)\n",
    "tmp_data[:5]\n",
    "start_date= 2\n",
    "tmp_df = pd.DataFrame(tmp_data,index=new_df.index[start_date:start_date+1000],columns=['tmp_df'])\n",
    "tmp_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\lib\\site-packages\\ipykernel\\__main__.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  if __name__ == '__main__':\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SP500</th>\n",
       "      <th>USDRMB</th>\n",
       "      <th>SHINDEX</th>\n",
       "      <th>SZINDEX</th>\n",
       "      <th>USDINDEX</th>\n",
       "      <th>EURUSD</th>\n",
       "      <th>tmp_df</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-01-04 00:00:00</th>\n",
       "      <td>1399.42</td>\n",
       "      <td>8.2798</td>\n",
       "      <td>1406.371</td>\n",
       "      <td>3497.060</td>\n",
       "      <td>114.3583</td>\n",
       "      <td>1.0295</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-05 00:00:00</th>\n",
       "      <td>1402.11</td>\n",
       "      <td>8.2799</td>\n",
       "      <td>1409.682</td>\n",
       "      <td>3486.285</td>\n",
       "      <td>114.6221</td>\n",
       "      <td>1.0316</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-06 00:00:00</th>\n",
       "      <td>1403.45</td>\n",
       "      <td>8.2798</td>\n",
       "      <td>1463.942</td>\n",
       "      <td>3655.199</td>\n",
       "      <td>115.1688</td>\n",
       "      <td>1.0295</td>\n",
       "      <td>-0.218224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-07 00:00:00</th>\n",
       "      <td>1441.47</td>\n",
       "      <td>8.2797</td>\n",
       "      <td>1516.604</td>\n",
       "      <td>3828.040</td>\n",
       "      <td>114.9098</td>\n",
       "      <td>1.0280</td>\n",
       "      <td>1.106715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-10 00:00:00</th>\n",
       "      <td>1457.60</td>\n",
       "      <td>8.2794</td>\n",
       "      <td>1545.112</td>\n",
       "      <td>3921.484</td>\n",
       "      <td>114.9799</td>\n",
       "      <td>1.0253</td>\n",
       "      <td>0.235592</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       SP500  USDRMB   SHINDEX   SZINDEX  USDINDEX  EURUSD  \\\n",
       "Date                                                                         \n",
       "2000-01-04 00:00:00  1399.42  8.2798  1406.371  3497.060  114.3583  1.0295   \n",
       "2000-01-05 00:00:00  1402.11  8.2799  1409.682  3486.285  114.6221  1.0316   \n",
       "2000-01-06 00:00:00  1403.45  8.2798  1463.942  3655.199  115.1688  1.0295   \n",
       "2000-01-07 00:00:00  1441.47  8.2797  1516.604  3828.040  114.9098  1.0280   \n",
       "2000-01-10 00:00:00  1457.60  8.2794  1545.112  3921.484  114.9799  1.0253   \n",
       "\n",
       "                       tmp_df  \n",
       "Date                           \n",
       "2000-01-04 00:00:00       NaN  \n",
       "2000-01-05 00:00:00       NaN  \n",
       "2000-01-06 00:00:00 -0.218224  \n",
       "2000-01-07 00:00:00  1.106715  \n",
       "2000-01-10 00:00:00  0.235592  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df['tmp_df']=tmp_df\n",
    "new_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: 'yyfdataout/new_df.csv'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-15-a93383bfeed8>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mnew_df\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"yyfdataout/new_df.csv\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mindex_label\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'date'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mnew_df\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_excel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"yyfdataout/new_df.xls\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mindex_label\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'date'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mnew_df\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_excel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"yyfdataout/new_df.xlsx\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mindex_label\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'date'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36mto_csv\u001b[1;34m(self, path_or_buf, sep, na_rep, float_format, columns, header, index, index_label, mode, encoding, compression, quoting, quotechar, line_terminator, chunksize, tupleize_cols, date_format, doublequote, escapechar, decimal)\u001b[0m\n\u001b[0;32m   1401\u001b[0m                                      \u001b[0mdoublequote\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdoublequote\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1402\u001b[0m                                      escapechar=escapechar, decimal=decimal)\n\u001b[1;32m-> 1403\u001b[1;33m         \u001b[0mformatter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msave\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1404\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1405\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mpath_or_buf\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Anaconda3\\lib\\site-packages\\pandas\\io\\formats\\format.py\u001b[0m in \u001b[0;36msave\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1575\u001b[0m             f, handles = _get_handle(self.path_or_buf, self.mode,\n\u001b[0;32m   1576\u001b[0m                                      \u001b[0mencoding\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mencoding\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1577\u001b[1;33m                                      compression=self.compression)\n\u001b[0m\u001b[0;32m   1578\u001b[0m             \u001b[0mclose\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1579\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Anaconda3\\lib\\site-packages\\pandas\\io\\common.py\u001b[0m in \u001b[0;36m_get_handle\u001b[1;34m(path_or_buf, mode, encoding, compression, memory_map, is_text)\u001b[0m\n\u001b[0;32m    383\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0mis_text\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    384\u001b[0m             \u001b[1;31m# Python 3 and no explicit encoding\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 385\u001b[1;33m             \u001b[0mf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpath_or_buf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'replace'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    386\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    387\u001b[0m             \u001b[1;31m# Python 3 and binary mode\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'yyfdataout/new_df.csv'"
     ]
    }
   ],
   "source": [
    "new_df.to_csv(\"yyfdataout/new_df.csv\",index_label='date')\n",
    "new_df.to_excel(\"yyfdataout/new_df.xls\",index_label='date')\n",
    "new_df.to_excel(\"yyfdataout/new_df.xlsx\",index_label='date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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